Foster customer engagement

Preventing churn is key to customer satisfaction and improving customer loyalty. The challenge is to detect the customers most likely to churn based on a Machine Learning model that assigns a score representing the probability of the customer's departure from the competition.

The customers with the highest churn score can then be the object of anti-churn marketing offers in order to improve customer satisfaction and avoid leaving the competition.

The implementation of the scoring model from construction to industrialization was done in six steps:

1. Data Engineering on CRM databases
   1. Descriptive analysis of the variables from the different databases: quality, completeness, consistency, distribution, correlations, etc.
   2. Cleaning and reprocessing. Data consolidation and preparation for analysis
2. Exploratory analysis of customer data
   1. Identification of first churners profiles
   2. Identification of early patterns of behaviour (patterns)
   3. Detection of discriminating variables
3. Scoring model training 
   1. Training and calibration of the supervised model
   2. Results testing and performance analysis
4. Validation of the scoring model
   1. Presentation of the model to marketing and customer service teams
   2. Verification of adequacy with business needs
   3. Adjustments and iterations
5. Industrialization of the scoring model in our client's IT infrastructure
   1. Delivery and deployment of an industrialized Python script for weekly prediction of upcoming deadlines.
6. Building an anti-churn marketing program with our experienced marketing and customer experience consultants and internal teams integrating the churn score.

Exploratory analysis of the marketing context and customer loyalty based on CRM data

Communication with the marketing teams in order to build the most relevant model from both a statistical and business point of view. Choice of the algorithm according to the case study and the marketing context.

Storage of the prediction model in order to facilitate the implementation in our client's IS

Collaboration with our team of marketing and customer experience consultants to build an anti churn marketing action plan based on the scoring model.

We obtain a churn prediction model with an accuracy of 85%. 

The attribution of a churn score per customer allows us to reduce the targeting scope of CRM campaigns, to avoid spam, to be more relevant and efficient on the message, but also to reduce the cost of the campaigns. 

The integration of the churn score in the customer advisor tools allows for better feedback to advisors on the churn risks for each customer, and thus a personalization of business practices.

Our teams of experts in Data Science and Customer Relationship Management will help you strengthen your customer relationship management and marketing strategy at every stage of the customer journey: from customer prospecting to preventing churn, increasing customer value and building loyalty. We intervene on different issues: market analysis, customer knowledge, customer segmentation. Detection of customer behaviour. Product recommendation.  Analysis and improvement of customer satisfaction and service. 

Our technical expertise is also complemented by a strong business knowledge thanks to our data scientists specialized in customer and marketing subjects and our Business Unit Marketing & Customer Experience, which allows us to respond to the specific issues of each of our customers.